Context-Based Online Garage Selling System
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Solution Overview
Problem
Sellers on online platforms face challenges in maximizing profit and minimizing operational costs when selling items during moves, as they receive multiple counteroffers from buyers, making it difficult to determine the best negotiation strategy amidst disposal and carrying costs.
Innovation Solution
A computer-implemented method for context-based online garage selling that determines seller and buyer contexts, generates buyer clusters, and provides optimal offer data to sellers, including suggested prices and counteroffer strategies, to facilitate efficient item disposal and minimize operational costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If sellers manually negotiate with multiple buyers on online platforms, then they can potentially maximize profit through individual counteroffers, but they face increased complexity in managing multiple negotiations and determining the best strategy amidst disposal and carrying costs
Solution Approach 1:
The system enables automated negotiation where the platform itself performs the complex task of evaluating counteroffers, comparing them against disposal and carrying costs, and determining optimal acceptance decisions. This self-service approach eliminates the need for sellers to manually manage multiple negotiations, thereby reducing operational complexity while maintaining profit maximization capabilities
Solution Approach 2:
The patent replaces the mechanical process of manual negotiation management with an automated computational system. The system uses algorithms to automatically evaluate counteroffers, calculate total costs including disposal and carrying expenses, and determine optimal negotiation strategies, substituting human cognitive and manual efforts with automated mechanical processes
2Productivity
If sellers list items for sale on online platforms, then they can reach potential buyers, but they receive multiple counteroffers that make it difficult to determine the best negotiation strategy
Solution Approach 1:
The system implements feedback mechanisms where counteroffers from multiple buyers are automatically collected, evaluated, and fed back to the seller with recommendations. The system analyzes each counteroffer against historical data, disposal costs, and carrying costs to provide feedback on which offers to accept or reject, thereby resolving the information loss problem and enabling faster item disposition
Solution Approach 2:
The online platform acts as an intermediary that mediates between multiple buyers and the seller. It automatically manages the flow of counteroffers, evaluates them against multiple criteria including disposal and carrying costs, and presents synthesized negotiation strategies to the seller, thereby preventing information loss and accelerating the disposition process
3Loss of energy
If sellers aim to maximize profit by evaluating each counteroffer individually, then they can potentially achieve higher sales revenue, but they increase the time required to complete negotiations and dispose of items
Solution Approach 1:
The system performs preliminary actions by pre-calculating disposal costs and carrying costs for each item before negotiations begin. This preliminary preparation allows the system to quickly evaluate counteroffers against known cost parameters, eliminating the need for time-consuming individual cost analyses during negotiations and enabling faster decision-making while maximizing profit
Solution Approach 2:
The system maintains continuous evaluation of counteroffers against disposal and carrying costs throughout the negotiation process. Rather than interrupting to recalculate costs for each offer, the system continuously monitors and evaluates all counteroffers against pre-established cost parameters, thereby reducing negotiation time while ensuring optimal profit maximization
Data Source
AI summary
Embodiments herein disclose computer-implemented methods, computer program products and computer systems for context based online garage offering. The computer-implemented method may include receiving listing data from the seller users corresponding to items offered for sale, wherein the one or more seller users and one or more buyer users are registered with a web-based exchange platform based on user registration data; determining a seller context for the items based on the user registration data and the listing data; receiving historical item data for the items; determining disposal costs versus carry costs for the items based on the historical item data; determine potential buyers based on the listing data and potential buyer activity of the buyers; determining a buyer context based on the user registration data and the potential buyer activity; generating offer data for the items; and presenting the offer data to the seller users via a computing device user interface.